import torch import transformers from transformers import PreTrainedTokenizerFast import tranception import datasets from tranception import config, model_pytorch import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import gradio as gr ####################################################################################################################################### ############################################### HELPER FUNCTIONS #################################################################### ####################################################################################################################################### AA_vocab = "ACDEFGHIKLMNPQRSTVWY" tokenizer = PreTrainedTokenizerFast(tokenizer_file="./tranception/utils/tokenizers/Basic_tokenizer", unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]" ) def create_all_single_mutants(sequence,AA_vocab=AA_vocab,mutation_range_start=None,mutation_range_end=None): all_single_mutants={} sequence_list=list(sequence) if mutation_range_start is None: mutation_range_start=1 if mutation_range_end is None: mutation_range_end=len(sequence) for position,current_AA in enumerate(sequence[mutation_range_start-1:mutation_range_end]): for mutated_AA in AA_vocab: if current_AA!=mutated_AA: mutated_sequence = sequence_list.copy() mutated_sequence[position] = mutated_AA all_single_mutants[current_AA+str(position+1)+mutated_AA]="".join(mutated_sequence) all_single_mutants = pd.DataFrame.from_dict(all_single_mutants,columns=['mutated_sequence'],orient='index') all_single_mutants.reset_index(inplace=True) all_single_mutants.columns = ['mutant','mutated_sequence'] return all_single_mutants def create_scoring_matrix_visual(scores,sequence,image_index=0,mutation_range_start=None,mutation_range_end=None,AA_vocab=AA_vocab,annotate=True,fontsize=20): filtered_scores=scores.copy() filtered_scores=filtered_scores[filtered_scores.position.isin(range(mutation_range_start,mutation_range_end+1))] piv=filtered_scores.pivot(index='position',columns='target_AA',values='avg_score').round(4) mutation_range_len = mutation_range_end - mutation_range_start + 1 fig, ax = plt.subplots(figsize=(50,mutation_range_len)) scores_dict = {} valid_mutant_set=set(filtered_scores.mutant) ax.tick_params(bottom=True, top=True, left=True, right=True) ax.tick_params(labelbottom=True, labeltop=True, labelleft=True, labelright=True) if annotate: for position in range(mutation_range_start,mutation_range_end+1): for target_AA in list(AA_vocab): mutant = sequence[position-1]+str(position)+target_AA if mutant in valid_mutant_set: scores_dict[mutant]= float(filtered_scores.loc[filtered_scores.mutant==mutant,'avg_score']) else: scores_dict[mutant]=0.0 labels = (np.asarray(["{} \n {:.4f}".format(symb,value) for symb, value in scores_dict.items() ])).reshape(mutation_range_len,len(AA_vocab)) heat = sns.heatmap(piv,annot=labels,fmt="",cmap='RdYlGn',linewidths=0.30,ax=ax,vmin=np.percentile(scores.avg_score,2),vmax=np.percentile(scores.avg_score,98),\ cbar_kws={'label': 'Log likelihood ratio (mutant / starting sequence)'},annot_kws={"size": fontsize}) else: heat = sns.heatmap(piv,cmap='RdYlGn',linewidths=0.30,ax=ax,vmin=np.percentile(scores.avg_score,2),vmax=np.percentile(scores.avg_score,98),\ cbar_kws={'label': 'Log likelihood ratio (mutant / starting sequence)'},annot_kws={"size": fontsize}) heat.figure.axes[-1].yaxis.label.set_size(fontsize=int(fontsize*1.5)) heat.figure.axes[-1].yaxis.set_ticklabels(heat.figure.axes[-1].yaxis.get_ticklabels(), fontsize=fontsize) heat.set_title("Higher predicted scores (green) imply higher protein fitness",fontsize=fontsize*2, pad=40) heat.set_ylabel("Sequence position", fontsize = fontsize*2) heat.set_xlabel("Amino Acid mutation", fontsize = fontsize*2) yticklabels = [str(pos)+' ('+sequence[pos-1]+')' for pos in range(mutation_range_start,mutation_range_end+1)] heat.set_yticklabels(yticklabels) heat.set_xticklabels(heat.get_xmajorticklabels(), fontsize = fontsize) heat.set_yticklabels(heat.get_ymajorticklabels(), fontsize = fontsize, rotation=0) plt.tight_layout() image_path = 'fitness_scoring_substitution_matrix_{}.png'.format(image_index) plt.savefig(image_path,dpi=100) plt.show() return image_path def suggest_mutations(scores): intro_message = "The following mutations may be sensible options to improve fitness: \n\n" #Best mutants top_mutants=list(scores.sort_values(by=['avg_score'],ascending=False).head(5).mutant) top_mutants_fitness=list(scores.sort_values(by=['avg_score'],ascending=False).head(5).avg_score) top_mutants_recos = [top_mutant+" ("+str(round(top_mutant_fitness,4))+")" for (top_mutant,top_mutant_fitness) in zip(top_mutants,top_mutants_fitness)] mutant_recos = "The single mutants with highest predicted fitness are (positive scores indicate fitness increase Vs starting sequence, negative scores indicate fitness decrease):\n {} \n\n".format(", ".join(top_mutants_recos)) #Best positions positive_scores = scores[scores.avg_score > 0] positive_scores_position_avg = positive_scores.groupby(['position']).mean() top_positions=list(positive_scores_position_avg.sort_values(by=['avg_score'],ascending=False).head(5).index.astype(str)) position_recos = "The positions with the highest average fitness increase are (only positions with at least one fitness increase are considered):\n {}".format(", ".join(top_positions)) return intro_message+mutant_recos+position_recos def check_valid_mutant(sequence,mutant,AA_vocab=AA_vocab): valid = True try: from_AA, position, to_AA = mutant[0], int(mutant[1:-1]), mutant[-1] except: valid = False if sequence[position-1]!=from_AA: valid=False if position<1 or position>len(sequence): valid=False if to_AA not in AA_vocab: valid=False return valid def get_mutated_protein(sequence,mutant): assert check_valid_mutant(sequence,mutant), "The mutant is not valid" mutated_sequence = list(sequence) mutated_sequence[int(mutant[1:-1])-1]=mutant[-1] return ''.join(mutated_sequence) def score_and_create_matrix_all_singles(sequence,mutation_range_start=None,mutation_range_end=None,model_type="Small",scoring_mirror=False,batch_size_inference=20,max_number_positions_per_heatmap=50,num_workers=0,AA_vocab=AA_vocab): if mutation_range_start is None: mutation_range_start=1 if mutation_range_end is None: mutation_range_end=len(sequence) assert len(sequence) > 0, "no sequence entered" assert mutation_range_start <= mutation_range_end, "mutation range is invalid" if model_type=="Small": model = tranception.model_pytorch.TranceptionLMHeadModel.from_pretrained(pretrained_model_name_or_path="PascalNotin/Tranception_Small") elif model_type=="Medium": model = tranception.model_pytorch.TranceptionLMHeadModel.from_pretrained(pretrained_model_name_or_path="PascalNotin/Tranception_Medium") elif model_type=="Large": model = tranception.model_pytorch.TranceptionLMHeadModel.from_pretrained(pretrained_model_name_or_path="PascalNotin/Tranception_Large") if torch.cuda.is_available(): model.cuda() print("Inference will take place on GPU") else: print("Inference will take place on CPU") model.config.tokenizer = tokenizer all_single_mutants = create_all_single_mutants(sequence,AA_vocab,mutation_range_start,mutation_range_end) scores = model.score_mutants(DMS_data=all_single_mutants, target_seq=sequence, scoring_mirror=scoring_mirror, batch_size_inference=batch_size_inference, num_workers=num_workers, indel_mode=False ) scores = pd.merge(scores,all_single_mutants,on="mutated_sequence",how="left") scores["position"]=scores["mutant"].map(lambda x: int(x[1:-1])) scores["target_AA"] = scores["mutant"].map(lambda x: x[-1]) score_heatmaps = [] mutation_range = mutation_range_end - mutation_range_start + 1 number_heatmaps = int((mutation_range - 1) / max_number_positions_per_heatmap) + 1 image_index = 0 window_start = mutation_range_start window_end = min(mutation_range_end,mutation_range_start+max_number_positions_per_heatmap-1) for image_index in range(number_heatmaps): score_heatmaps.append(create_scoring_matrix_visual(scores,sequence,image_index,window_start,window_end,AA_vocab)) window_start += max_number_positions_per_heatmap window_end = min(mutation_range_end,window_start+max_number_positions_per_heatmap-1) return score_heatmaps, suggest_mutations(scores) def extract_sequence(example): label, taxon, sequence = example return sequence def clear_inputs(protein_sequence_input,mutation_range_start,mutation_range_end): protein_sequence_input = "" mutation_range_start = None mutation_range_end = None return protein_sequence_input,mutation_range_start,mutation_range_end ####################################################################################################################################### ############################################### GRADIO INTERFACE #################################################################### ####################################################################################################################################### tranception_design = gr.Blocks() with tranception_design: gr.Markdown("# In silico directed evolution for protein redesign with Tranception") gr.Markdown(" Perform in silico directed evolution with Tranception to iteratively improve the fitness of a protein of interest, one mutation at a time. At each step, the Tranception model computes the log likelihood ratios of all possible single amino acid substitution Vs the starting sequence, and outputs a fitness heatmap and recommandations to guide the selection of the mutation to apply.") with gr.Tabs(): with gr.TabItem("Input"): with gr.Row(): protein_sequence_input = gr.Textbox(lines=1, label="Protein sequence", placeholder = "Input the sequence of amino acids representing the starting protein of interest or select one from the list of examples below. You may enter the full sequence or just a subdomain (providing full context typically leads to better results, but is slower at inference)" ) with gr.Row(): mutation_range_start = gr.Number(label="Start of mutation window (first position indexed at 1)", value=1, precision=0) mutation_range_end = gr.Number(label="End of mutation window (leave empty for full lenth)", value=10, precision=0) with gr.TabItem("Parameters"): with gr.Row(): model_size_selection = gr.Radio(label="Tranception model size (larger models are more accurate but are slower at inference)", choices=["Small","Medium","Large"], value="Small") with gr.Row(): scoring_mirror = gr.Checkbox(label="Score protein from both directions (leads to more robust fitness predictions, but doubles inference time)") with gr.Row(): batch_size_inference = gr.Number(label="Model batch size at inference time",value = 100, precision=0) with gr.Row(): gr.Markdown("Note: the current version does not leverage retrieval of homologs at inference time to increase fitness prediction performance.") with gr.Row(): clear_button = gr.Button(value="Clear",variant="secondary") run_button = gr.Button(value="Predict fitness",variant="primary") protein_ID = gr.Textbox(label="Uniprot ID", visible=False) taxon = gr.Textbox(label="Taxon", visible=False) examples = gr.Examples( inputs=[protein_ID, taxon, protein_sequence_input], outputs=[protein_sequence_input], fn=extract_sequence, examples=[ ['ADRB2_HUMAN' ,'Human', 'MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL'], ['IF1_ECOLI' ,'Prokaryote', 'MAKEDNIEMQGTVLETLPNTMFRVELENGHVVTAHISGKMRKNYIRILTGDKVTVELTPYDLSKGRIVFRSR'], ['P53_HUMAN' ,'Human', 'MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPRVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD'], ['BLAT_ECOLX' ,'Prokaryote', 'MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIELDLNSGKILESFRPEERFPMMSTFKVLLCGAVLSRVDAGQEQLGRRIHYSQNDLVEYSPVTEKHLTDGMTVRELCSAAITMSDNTAANLLLTTIGGPKELTAFLHNMGDHVTRLDRWEPELNEAIPNDERDTTMPAAMATTLRKLLTGELLTLASRQQLIDWMEADKVAGPLLRSALPAGWFIADKSGAGERGSRGIIAALGPDGKPSRIVVIYTTGSQATMDERNRQIAEIGASLIKHW'], ['BRCA1_HUMAN' ,'Human', 'MDLSALRVEEVQNVINAMQKILECPICLELIKEPVSTKCDHIFCKFCMLKLLNQKKGPSQCPLCKNDITKRSLQESTRFSQLVEELLKIICAFQLDTGLEYANSYNFAKKENNSPEHLKDEVSIIQSMGYRNRAKRLLQSEPENPSLQETSLSVQLSNLGTVRTLRTKQRIQPQKTSVYIELGSDSSEDTVNKATYCSVGDQELLQITPQGTRDEISLDSAKKAACEFSETDVTNTEHHQPSNNDLNTTEKRAAERHPEKYQGSSVSNLHVEPCGTNTHASSLQHENSSLLLTKDRMNVEKAEFCNKSKQPGLARSQHNRWAGSKETCNDRRTPSTEKKVDLNADPLCERKEWNKQKLPCSENPRDTEDVPWITLNSSIQKVNEWFSRSDELLGSDDSHDGESESNAKVADVLDVLNEVDEYSGSSEKIDLLASDPHEALICKSERVHSKSVESNIEDKIFGKTYRKKASLPNLSHVTENLIIGAFVTEPQIIQERPLTNKLKRKRRPTSGLHPEDFIKKADLAVQKTPEMINQGTNQTEQNGQVMNITNSGHENKTKGDSIQNEKNPNPIESLEKESAFKTKAEPISSSISNMELELNIHNSKAPKKNRLRRKSSTRHIHALELVVSRNLSPPNCTELQIDSCSSSEEIKKKKYNQMPVRHSRNLQLMEGKEPATGAKKSNKPNEQTSKRHDSDTFPELKLTNAPGSFTKCSNTSELKEFVNPSLPREEKEEKLETVKVSNNAEDPKDLMLSGERVLQTERSVESSSISLVPGTDYGTQESISLLEVSTLGKAKTEPNKCVSQCAAFENPKGLIHGCSKDNRNDTEGFKYPLGHEVNHSRETSIEMEESELDAQYLQNTFKVSKRQSFAPFSNPGNAEEECATFSAHSGSLKKQSPKVTFECEQKEENQGKNESNIKPVQTVNITAGFPVVGQKDKPVDNAKCSIKGGSRFCLSSQFRGNETGLITPNKHGLLQNPYRIPPLFPIKSFVKTKCKKNLLEENFEEHSMSPEREMGNENIPSTVSTISRNNIRENVFKEASSSNINEVGSSTNEVGSSINEIGSSDENIQAELGRNRGPKLNAMLRLGVLQPEVYKQSLPGSNCKHPEIKKQEYEEVVQTVNTDFSPYLISDNLEQPMGSSHASQVCSETPDDLLDDGEIKEDTSFAENDIKESSAVFSKSVQKGELSRSPSPFTHTHLAQGYRRGAKKLESSEENLSSEDEELPCFQHLLFGKVNNIPSQSTRHSTVATECLSKNTEENLLSLKNSLNDCSNQVILAKASQEHHLSEETKCSASLFSSQCSELEDLTANTNTQDPFLIGSSKQMRHQSESQGVGLSDKELVSDDEERGTGLEENNQEEQSMDSNLGEAASGCESETSVSEDCSGLSSQSDILTTQQRDTMQHNLIKLQQEMAELEAVLEQHGSQPSNSYPSIISDSSALEDLRNPEQSTSEKAVLTSQKSSEYPISQNPEGLSADKFEVSADSSTSKNKEPGVERSSPSKCPSLDDRWYMHSCSGSLQNRNYPSQEELIKVVDVEEQQLEESGPHDLTETSYLPRQDLEGTPYLESGISLFSDDPESDPSEDRAPESARVGNIPSSTSALKVPQLKVAESAQSPAAAHTTDTAGYNAMEESVSREKPELTASTERVNKRMSMVVSGLTPEEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPKRARESQDRKIFRGLEICCYGPFTNMPTDQLEWMVQLCGASVVKELSSFTLGTGVHPIVVVQPDAWTEDNGFHAIGQMCEAPVVTREWVLDSVALYQCQELDTYLIPQIPHSHY'], ['CALM1_HUMAN' ,'Human', 'MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGNGTIDFPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIREADIDGDGQVNYEEFVQMMTAK'], ['CCDB_ECOLI' ,'Prokaryote', 'MQFKVYTYKRESRYRLFVDVQSDIIDTPGRRMVIPLASARLLSDKVSRELYPVVHIGDESWRMMTTDMASVPVSVIGEEVADLSHRENDIKNAINLMFWGI'], ['GFP_AEQVI' ,'Other eukaryote', 'MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK'], ['GRB2_HUMAN' ,'Human', 'MEAIAKYDFKATADDELSFKRGDILKVLNEECDQNWYKAELNGKDGFIPKNYIEMKPHPWFFGKIPRAKAEEMLSKQRHDGAFLIRESESAPGDFSLSVKFGNDVQHFKVLRDGAGKYFLWVVKFNSLNELVDYHRSTSVSRNQQIFLRDIEQVPQQPTYVQALFDFDPQEDGELGFRRGDFIHVMDNSDPNWWKGACHGQTGMFPRNYVTPVNRNV'], ['HSP82_YEAST' ,'Eukaryote ', 'MASETFEFQAEITQLMSLIINTVYSNKEIFLRELISNASDALDKIRYKSLSDPKQLETEPDLFIRITPKPEQKVLEIRDSGIGMTKAELINNLGTIAKSGTKAFMEALSAGADVSMIGQFGVGFYSLFLVADRVQVISKSNDDEQYIWESNAGGSFTVTLDEVNERIGRGTILRLFLKDDQLEYLEEKRIKEVIKRHSEFVAYPIQLVVTKEVEKEVPIPEEEKKDEEKKDEEKKDEDDKKPKLEEVDEEEEKKPKTKKVKEEVQEIEELNKTKPLWTRNPSDITQEEYNAFYKSISNDWEDPLYVKHFSVEGQLEFRAILFIPKRAPFDLFESKKKKNNIKLYVRRVFITDEAEDLIPEWLSFVKGVVDSEDLPLNLSREMLQQNKIMKVIRKNIVKKLIEAFNEIAEDSEQFEKFYSAFSKNIKLGVHEDTQNRAALAKLLRYNSTKSVDELTSLTDYVTRMPEHQKNIYYITGESLKAVEKSPFLDALKAKNFEVLFLTDPIDEYAFTQLKEFEGKTLVDITKDFELEETDEEKAEREKEIKEYEPLTKALKEILGDQVEKVVVSYKLLDAPAAIRTGQFGWSANMERIMKAQALRDSSMSSYMSSKKTFEISPKSPIIKELKKRVDEGGAQDKTVKDLTKLLYETALLTSGFSLDEPTSFASRINRLISLGLNIDEDEETETAPEASTAAPVEEVPADTEMEEVD'], ['IF1_ECOLI' ,'Prokaryote', 'MAKEDNIEMQGTVLETLPNTMFRVELENGHVVTAHISGKMRKNYIRILTGDKVTVELTPYDLSKGRIVFRSR'], ['KCNH2_HUMAN' ,'Human', 'MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS'], ['KKA2_KLEPN' ,'Prokaryote', 'MIEQDGLHAGSPAAWVERLFGYDWAQQTIGCSDAAVFRLSAQGRPVLFVKTDLSGALNELQDEAARLSWLATTGVPCAAVLDVVTEAGRDWLLLGEVPGQDLLSSHLAPAEKVSIMADAMRRLHTLDPATCPFDHQAKHRIERARTRMEAGLVDQDDLDEEHQGLAPAELFARLKARMPDGEDLVVTHGDACLPNIMVENGRFSGFIDCGRLGVADRYQDIALATRDIAEELGGEWADRFLVLYGIAAPDSQRIAFYRLLDEFF'], ['MSH2_HUMAN' ,'Human', 'MAVQPKETLQLESAAEVGFVRFFQGMPEKPTTTVRLFDRGDFYTAHGEDALLAAREVFKTQGVIKYMGPAGAKNLQSVVLSKMNFESFVKDLLLVRQYRVEVYKNRAGNKASKENDWYLAYKASPGNLSQFEDILFGNNDMSASIGVVGVKMSAVDGQRQVGVGYVDSIQRKLGLCEFPDNDQFSNLEALLIQIGPKECVLPGGETAGDMGKLRQIIQRGGILITERKKADFSTKDIYQDLNRLLKGKKGEQMNSAVLPEMENQVAVSSLSAVIKFLELLSDDSNFGQFELTTFDFSQYMKLDIAAVRALNLFQGSVEDTTGSQSLAALLNKCKTPQGQRLVNQWIKQPLMDKNRIEERLNLVEAFVEDAELRQTLQEDLLRRFPDLNRLAKKFQRQAANLQDCYRLYQGINQLPNVIQALEKHEGKHQKLLLAVFVTPLTDLRSDFSKFQEMIETTLDMDQVENHEFLVKPSFDPNLSELREIMNDLEKKMQSTLISAARDLGLDPGKQIKLDSSAQFGYYFRVTCKEEKVLRNNKNFSTVDIQKNGVKFTNSKLTSLNEEYTKNKTEYEEAQDAIVKEIVNISSGYVEPMQTLNDVLAQLDAVVSFAHVSNGAPVPYVRPAILEKGQGRIILKASRHACVEVQDEIAFIPNDVYFEKDKQMFHIITGPNMGGKSTYIRQTGVIVLMAQIGCFVPCESAEVSIVDCILARVGAGDSQLKGVSTFMAEMLETASILRSATKDSLIIIDELGRGTSTYDGFGLAWAISEYIATKIGAFCMFATHFHELTALANQIPTVNNLHVTALTTEETLTMLYQVKKGVCDQSFGIHVAELANFPKHVIECAKQKALELEEFQYIGESQGYDIMEPAAKKCYLEREQGEKIIQEFLSKVKQMPFTEMSEENITIKLKQLKAEVIAKNNSFVNEIISRIKVTT'], ['PABP_YEAST' ,'Other eukaryote', 'MADITDKTAEQLENLNIQDDQKQAATGSESQSVENSSASLYVGDLEPSVSEAHLYDIFSPIGSVSSIRVCRDAITKTSLGYAYVNFNDHEAGRKAIEQLNYTPIKGRLCRIMWSQRDPSLRKKGSGNIFIKNLHPDIDNKALYDTFSVFGDILSSKIATDENGKSKGFGFVHFEEEGAAKEAIDALNGMLLNGQEIYVAPHLSRKERDSQLEETKAHYTNLYVKNINSETTDEQFQELFAKFGPIVSASLEKDADGKLKGFGFVNYEKHEDAVKAVEALNDSELNGEKLYVGRAQKKNERMHVLKKQYEAYRLEKMAKYQGVNLFVKNLDDSVDDEKLEEEFAPYGTITSAKVMRTENGKSKGFGFVCFSTPEEATKAITEKNQQIVAGKPLYVAIAQRKDVRRSQLAQQIQARNQMRYQQATAAAAAAAAGMPGQFMPPMFYGVMPPRGVPFNGPNPQQMNPMGGMPKNGMPPQFRNGPVYGVPPQGGFPRNANDNNQFYQQKQRQALGEQLYKKVSAKTSNEEAAGKITGMILDLPPQEVFPLLESDELFEQHYKEASAAYESFKKEQEQQTEQA'], ['P53_HUMAN' ,'Human', 'MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPRVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD'], ['PTEN_HUMAN' ,'Human', 'MTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCAERHYDTAKFNCRVAQYPFEDHNPPQLELIKPFCEDLDQWLSEDDNHVAAIHCKAGKGRTGVMICAYLLHRGKFLKAQEALDFYGEVRTRDKKGVTIPSQRRYVYYYSYLLKNHLDYRPVALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVKLYFTKTVEEPSNPEASSSTSVTPDVSDNEPDHYRYSDTTDSDPENEPFDEDQHTQITKV'], ['RL40A_YEAST' ,'Eukaryote ', 'MQIFVKTLTGKTITLEVESSDTIDNVKSKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGGIIEPSLKALASKYNCDKSVCRKCYARLPPRATNCRKRKCGHTNQLRPKKKLK'], ['SCN5A_HUMAN' ,'Human', 'MANFLLPRGTSSFRRFTRESLAAIEKRMAEKQARGSTTLQESREGLPEEEAPRPQLDLQASKKLPDLYGNPPQELIGEPLEDLDPFYSTQKTFIVLNKGKTIFRFSATNALYVLSPFHPIRRAAVKILVHSLFNMLIMCTILTNCVFMAQHDPPPWTKYVEYTFTAIYTFESLVKILARGFCLHAFTFLRDPWNWLDFSVIIMAYTTEFVDLGNVSALRTFRVLRALKTISVISGLKTIVGALIQSVKKLADVMVLTVFCLSVFALIGLQLFMGNLRHKCVRNFTALNGTNGSVEADGLVWESLDLYLSDPENYLLKNGTSDVLLCGNSSDAGTCPEGYRCLKAGENPDHGYTSFDSFAWAFLALFRLMTQDCWERLYQQTLRSAGKIYMIFFMLVIFLGSFYLVNLILAVVAMAYEEQNQATIAETEEKEKRFQEAMEMLKKEHEALTIRGVDTVSRSSLEMSPLAPVNSHERRSKRRKRMSSGTEECGEDRLPKSDSEDGPRAMNHLSLTRGLSRTSMKPRSSRGSIFTFRRRDLGSEADFADDENSTAGESESHHTSLLVPWPLRRTSAQGQPSPGTSAPGHALHGKKNSTVDCNGVVSLLGAGDPEATSPGSHLLRPVMLEHPPDTTTPSEEPGGPQMLTSQAPCVDGFEEPGARQRALSAVSVLTSALEELEESRHKCPPCWNRLAQRYLIWECCPLWMSIKQGVKLVVMDPFTDLTITMCIVLNTLFMALEHYNMTSEFEEMLQVGNLVFTGIFTAEMTFKIIALDPYYYFQQGWNIFDSIIVILSLMELGLSRMSNLSVLRSFRLLRVFKLAKSWPTLNTLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKNYSELRDSDSGLLPRWHMMDFFHAFLIIFRILCGEWIETMWDCMEVSGQSLCLLVFLLVMVIGNLVVLNLFLALLLSSFSADNLTAPDEDREMNNLQLALARIQRGLRFVKRTTWDFCCGLLRQRPQKPAALAAQGQLPSCIATPYSPPPPETEKVPPTRKETRFEEGEQPGQGTPGDPEPVCVPIAVAESDTDDQEEDEENSLGTEEESSKQQESQPVSGGPEAPPDSRTWSQVSATASSEAEASASQADWRQQWKAEPQAPGCGETPEDSCSEGSTADMTNTAELLEQIPDLGQDVKDPEDCFTEGCVRRCPCCAVDTTQAPGKVWWRLRKTCYHIVEHSWFETFIIFMILLSSGALAFEDIYLEERKTIKVLLEYADKMFTYVFVLEMLLKWVAYGFKKYFTNAWCWLDFLIVDVSLVSLVANTLGFAEMGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFGRCINQTEGDLPLNYTIVNNKSQCESLNLTGELYWTKVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRGYEEQPQWEYNLYMYIYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPLNKYQGFIFDIVTKQAFDVTIMFLICLNMVTMMVETDDQSPEKINILAKINLLFVAIFTGECIVKLAALRHYYFTNSWNIFDFVVVILSIVGTVLSDIIQKYFFSPTLFRVIRLARIGRILRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMANFAYVKWEAGIDDMFNFQTFANSMLCLFQITTSAGWDGLLSPILNTGPPYCDPTLPNSNGSRGDCGSPAVGILFFTTYIIISFLIVVNMYIAIILENFSVATEESTEPLSEDDFDMFYEIWEKFDPEATQFIEYSVLSDFADALSEPLRIAKPNQISLINMDLPMVSGDRIHCMDILFAFTKRVLGESGEMDALKIQMEEKFMAANPSKISYEPITTTLRRKHEEVSAMVIQRAFRRHLLQRSLKHASFLFRQQAGSGLSEEDAPEREGLIAYVMSENFSRPLGPPSSSSISSTSFPPSYDSVTRATSDNLQVRGSDYSHSEDLADFPPSPDRDRESIV'], ['SUMO1_HUMAN' ,'Human', 'MSDQEAKPSTEDLGDKKEGEYIKLKVIGQDSSEIHFKVKMTTHLKKLKESYCQRQGVPMNSLRFLFEGQRIADNHTPKELGMEEEDVIEVYQEQTGGHSTV'] ], ) gr.Markdown("
") gr.Markdown("# Fitness predictions for all single amino acid substitutions in mutation range") gr.Markdown("Inference may take a few seconds for short proteins & mutation ranges to several minutes for longer ones") output_image = gr.Gallery(label="Fitness predictions for all single amino acid substitutions in mutation range",type="filepath") #Using Gallery to break down large scoring matrices into smaller images output_recommendations = gr.Textbox(label="Mutation recommendations") clear_button.click( inputs = [protein_sequence_input,mutation_range_start,mutation_range_end], outputs = [protein_sequence_input,mutation_range_start,mutation_range_end], fn=clear_inputs ) run_button.click( fn=score_and_create_matrix_all_singles, inputs=[protein_sequence_input,mutation_range_start,mutation_range_end,model_size_selection,scoring_mirror,batch_size_inference], outputs=[output_image,output_recommendations], ) gr.Markdown("# Mutate the starting protein sequence") with gr.Row(): mutation_triplet = gr.Textbox(lines=1,label="Selected mutation", placeholder = "Input the mutation triplet for the selected mutation (eg., M1A)") mutate_button = gr.Button(value="Apply mutation to starting protein", variant="primary") mutated_protein_sequence = gr.Textbox(lines=1,label="Mutated protein sequence") mutate_button.click( fn = get_mutated_protein, inputs = [protein_sequence_input,mutation_triplet], outputs = mutated_protein_sequence ) gr.Markdown("

You may now use the output mutated sequence above as the starting sequence for another round of in silico directed evolution.

") gr.Markdown("For more information about the Tranception model, please refer to our paper below:") gr.Markdown("

Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval
Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks*, Yarin Gal*
* equal senior authorship

") gr.Markdown("Links: Paper Code ProteinGym") tranception_design.launch(debug=True)